PLAYING WHILE FEMALE: RE-READING IDENTITY FIXATIONS IN OVERWATCH
Bibliographic record
Abstract
In early 2019, Overwatch professional player, “Ellie” quit playing just weeks after having been named to one of the teams seeded into the professional league. The harassment cited as a reason to leave was related especially to whether or not “Ellie” was truly “female”. Not much later, Ellie was revealed (and confirmed by Blizzard, the parent company of Overwatch) to be an account created by a male player. This paper sets out to map the controversy that ensued from a self-styled “social experiment” of playing while female.
 This paper brings this current “revelation” into conversation with past, more fully embodied/manufactured identities to better understand why this case is particularly important to internet studies. To this end, we begin by briefly describing some earlier, more familiar cases of people revealed to be someone other than, in online spaces, they said they were. Then, we further outline the instance of Ellie: its uptake by mainstream media, prominent Youtubers and Twitch streamers, and its discussion on internet forums like Reddit and 4Chan. Paying particular attention to the ways these discussions frame the “trick” played in disguising Ellie’s ‘true’ identity (singular), we suggest that this kind of case has always been galvanized by an underlying conviction that the best gamers are always and only men, and one contribution internet scholarship can make here is to show how these discursive patterns are unhelpful in understanding contemporary identificatory politics and practices in online spaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".